(untitled) (International Journal of Advanced Research in Computer Science & Technology(IJARCST), 2023)
The rapid adoption of hybrid cloud environments has transformed enterprise information technology architectures by combining private infrastructure, public cloud platforms, edge computing resources, and interconnected application ecosystems. However, this transformation has introduced complex security challenges involving fragmented governance, dynamic threat landscapes, API vulnerabilities, regulatory compliance requirements, and difficulties in maintaining consistent enterprise-wide security controls. Machine learning (ML)-driven enterprise architecture provides an intelligent approach for addressing these challenges by embedding predictive analytics, automated decision-making, and adaptive security mechanisms into hybrid cloud ecosystems. This research explores the integration of machine learning capabilities with enterprise architecture frameworks to enhance hybrid cloud security, API integration management, and regulatory intelligence. The study examines how ML techniques such as anomaly detection, natural language processing, automated compliance monitoring, and predictive risk analysis can support organizations in achieving resilient and adaptive security architectures. The research also investigates the role of intelligent API management in enabling secure interoperability between cloud services, enterprise applications, and regulatory intelligence platforms. A conceptual research methodology based on qualitative analysis, architectural evaluation, and synthesis of existing scholarly and industry practices is adopted to examine the relationship between machine learning, enterprise architecture, and hybrid cloud governance. The findings highlight that ML-enabled enterprise architectures can improve threat detection, compliance automation, security orchestration, and strategic decision-making while supporting continuous adaptation to evolving regulatory and cybersecurity environments